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Seo, J.K.[Jin Keun],
Mathematical Framework for Abdominal Electrical Impedance Tomography
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SIIMS(10), No. 2, 2017, pp. 900-919.
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1708
BibRef
Gong, B.,
Schullcke, B.,
Krueger-Ziolek, S.,
Vauhkonen, M.,
Wolf, G.,
Mueller-Lisse, U.,
Moeller, K.,
EIT Imaging Regularization Based on Spectral Graph Wavelets,
MedImg(36), No. 9, September 2017, pp. 1832-1844.
IEEE DOI
1709
bioelectric phenomena, biological tissues,
electric impedance imaging,
wavelet transforms, wavelet transforms, Jacobian matrices,
Electricalimpedancetomography
BibRef
Liu, D.,
Khambampati, A.K.,
Du, J.,
A Parametric Level Set Method for Electrical Impedance Tomography,
MedImg(37), No. 2, February 2018, pp. 451-460.
IEEE DOI
1802
Conductivity, Image reconstruction, Inverse problems, Level set,
Lungs, Shape, Tomography, Electrical impedance tomography,
parametric level set method
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Garmatter, D.[Dominik],
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Magnetic Resonance Electrical Impedance Tomography (MREIT):
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BibRef
Liu, S.,
Jia, J.,
Zhang, Y.D.,
Yang, Y.,
Image Reconstruction in Electrical Impedance Tomography Based on
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MedImg(37), No. 9, September 2018, pp. 2090-2102.
IEEE DOI
1809
Tomography, Image reconstruction, Bayes methods, Electrodes,
Voltage measurement, Conductivity, Computational modeling,
maximuma posteriori(MAP) estimation
BibRef
Leijsen, R.L.,
Brink, W.M.,
van den Berg, C.A.T.,
Webb, A.G.,
Remis, R.F.,
3-D Contrast Source Inversion-Electrical Properties Tomography,
MedImg(37), No. 9, September 2018, pp. 2080-2089.
IEEE DOI
1809
Radio frequency, Mathematical model,
Image reconstruction, Magnetic resonance imaging,
electromagnetic Green's tensor field representations
BibRef
Lee, K.,
Woo, E.J.,
Seo, J.K.,
A Fidelity-Embedded Regularization Method for Robust Electrical
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MedImg(37), No. 9, September 2018, pp. 1970-1977.
IEEE DOI
1809
Electrodes, Conductivity, Tomography, Image reconstruction,
Voltage measurement, Jacobian matrices,
motion artifacts reduction
BibRef
Hamilton, S.J.,
Hauptmann, A.,
Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With
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MedImg(37), No. 10, October 2018, pp. 2367-2377.
IEEE DOI
1810
Tomography, Image reconstruction, Conductivity, Real-time systems,
Impedance, Robustness, Current measurement,
conductivity imaging
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Liu, D.,
Smyl, D.,
Du, J.,
A Parametric Level Set-Based Approach to Difference Imaging in
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MedImg(38), No. 1, January 2019, pp. 145-155.
IEEE DOI
1901
Conductivity, Tomography, Image reconstruction,
Computational modeling, Level set, Conductivity measurement,
inverse problems
BibRef
Liu, D.,
Gu, D.,
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Deng, J.,
Du, J.,
B-Spline Level Set Method for Shape Reconstruction in Electrical
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IEEE DOI
2006
Electrical impedance tomography, B-spline level set method,
Inverse problems, Shape reconstruction
BibRef
Liu, D.,
Gu, D.,
Smyl, D.,
Deng, J.,
Du, J.,
Shape Reconstruction Using Boolean Operations in Electrical Impedance
Tomography,
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IEEE DOI
2009
Shape, Image reconstruction, Conductivity, Splines (mathematics),
Level set, Reconstruction algorithms,
lung imaging
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Ren, S.,
Sun, K.,
Liu, D.,
Dong, F.,
A Statistical Shape-Constrained Reconstruction Framework for
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MedImg(38), No. 10, October 2019, pp. 2400-2410.
IEEE DOI
1910
Shape, Lung, Image reconstruction, Computed tomography, Conductivity,
Electrical impedance tomography, lung imaging,
robust principal component analysis
BibRef
Liu, D.,
Gu, D.,
Smyl, D.,
Deng, J.,
Du, J.,
B-Spline-Based Sharp Feature Preserving Shape Reconstruction Approach
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MedImg(38), No. 11, November 2019, pp. 2533-2544.
IEEE DOI
1911
Tomography, Splines (mathematics), Shape, Image reconstruction,
Conductivity, Fourier series, Numerical models,
inverse problems
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Liu, D.,
Du, J.,
A Moving Morphable Components Based Shape Reconstruction Framework
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MedImg(38), No. 12, December 2019, pp. 2937-2948.
IEEE DOI
1912
Shape, Image reconstruction, Tomography, Topology, Level set,
Conductivity, Electrodes, Electrical impedance tomography,
inverse problems
BibRef
Jiang, Y.D.,
Soleimani, M.,
Capacitively Coupled Electrical Impedance Tomography for Brain
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MedImg(38), No. 9, September 2019, pp. 2104-2113.
IEEE DOI
1909
Electrodes, Tomography, Impedance, Head, Conductivity,
Electrical resistance measurement,
multi-frequency time-difference imaging
BibRef
Connell, I.R.O.,
Menon, R.S.,
Shape Optimization of an Electric Dipole Array for 7 Tesla
Neuroimaging,
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IEEE DOI
1909
Radio frequency, Optimization, Conductors, Shape, Head, Couplings,
Impedance, Radio-frequency arrays, RF arrays, MRI RF coils, dipoles,
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Liang, G.,
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2002
Dual-modality imaging, inclusion boundary reconstruction,
electrical impedance tomography,
statistical inversion
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Wei, Z.,
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2005
Tomography, Conductivity, Mathematical model, Image reconstruction,
Inverse problems, Pollution measurement, Electrodes,
convolutional neural network
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Li, Z.,
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2006
Computed tomography, cross-gradient function,
electrical impedance tomography, lung imaging
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Jauhiainen, J.[Jyrki],
Kuusela, P.[Petri],
Seppänen, A.[Aku],
Valkonen, T.[Tuomo],
Relaxed Gauss-Newton Methods with Applications to Electrical
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SIIMS(13), No. 3, 2020, pp. 1415-1445.
DOI Link
2010
BibRef
Murphy, E.K.,
Wu, X.,
Everitt, A.C.,
Halter, R.J.,
Phantom Studies of Fused-Data TREIT Using Only Biopsy-Probe
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2011
Electrodes, Biopsy, Probes, Tomography, Image reconstruction,
Conductivity, Data fusion, electrical impedance tomography,
bioimpedance
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Liu, D.,
Smyl, D.,
Gu, D.,
Du, J.,
Shape-Driven Difference Electrical Impedance Tomography,
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IEEE DOI
2012
Tomography, Conductivity, Image reconstruction, Shape,
Computational modeling, Conductivity measurement, shape reconstruction
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Liu, D.,
Gu, D.,
Smyl, D.,
Khambampati, A.K.,
Deng, J.,
Du, J.,
Shape-Driven EIT Reconstruction Using Fourier Representations,
MedImg(40), No. 2, February 2021, pp. 481-490.
IEEE DOI
2102
Shape, Image reconstruction, Tomography, Conductivity, Topology,
Conductivity measurement, Current measurement, primitives
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Bar, L.[Leah],
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Strong Solutions for PDE-Based Tomography by Unsupervised Learning,
SIIMS(14), No. 1, 2021, pp. 128-155.
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BibRef
Smyl, D.[Danny],
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An Efficient Quasi-Newton Method for Nonlinear Inverse Problems via
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IEEE DOI
2105
Jacobian matrices, Artificial neural networks, Inverse problems,
Training, Computational modeling,
electrical impedance tomography
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Agnelli, J.P.[Juan P.],
Kolehmainen, V.[Ville],
Lassas, M.J.[Matti J.],
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2112
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Wang, Y.[Yaru],
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Conductivity characteristics of human lung tissues,
IJIST(32), No. 1, 2022, pp. 178-191.
DOI Link
2201
conductivity characteristics, electrical impedance tomography,
human lung tissue, impedance spectra, priori information
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Shi, Y.Y.[Yan-Yan],
Tian, Z.W.[Zhi-Wei],
Wang, M.[Meng],
Rao, Z.G.[Zu-Guang],
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Reconstruction of conductivity distribution with a compound
variational strategy in electrical impedance tomography,
IJIST(32), No. 1, 2022, pp. 295-306.
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2201
conductivity distribution, electrical impedance tomography, visualization
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Yanyan, S.[Shi],
Yuhang, Z.[Zhang],
Meng, W.[Wang],
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Meng, D.[Dai],
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Image reconstruction based on a modified bird swarm optimization
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IJIST(33), No. 3, 2023, pp. 1062-1072.
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2305
conductivity distribution, electrical impedance tomography,
image reconstruction, modified birds swarm algorithm.
BibRef
Liu, D.[Dong],
Wang, J.[Junwu],
Shan, Q.X.[Qian-Xue],
Smyl, D.[Danny],
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Du, J.F.[Jiang-Feng],
DeepEIT: Deep Image Prior Enabled Electrical Impedance Tomography,
PAMI(45), No. 8, August 2023, pp. 9627-9638.
IEEE DOI
2307
Electrical impedance tomography, Image reconstruction,
Electronics packaging, Artificial neural networks, Training data,
unsupervised learning
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Madjid, N.A.,
Liatsis, P.,
Object Segmentation In Electrical Impedance Tomography For Tactile
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ICIP20(3050-3054)
IEEE DOI
2011
Image segmentation, Tomography, Image reconstruction, Sensors,
Electrodes, Mathematical model, Conductivity, Active contours,
tactile sensing
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Li, X.,
Lu, Y.,
Wang, J.,
Dang, X.,
Wang, Q.,
Duan, X.,
Sun, Y.,
An image reconstruction framework based on deep neural network for
electrical impedance tomography,
ICIP17(3585-3589)
IEEE DOI
1803
Conductivity, Finite element analysis, Image reconstruction,
Mathematical model, Tomography, Training, Voltage measurement,
stacked autoencoder (SAE)
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Zaravi, S.,
Amirfattahi, R.,
Vahdat, B.V.,
Investigation of error propagation and measurement error for 2D block
method in Electrical Impedance Tomography,
IPRIA15(1-4)
IEEE DOI
1603
bioelectric potentials
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Lu, L.[Lin],
Liu, L.[Lili],
Hu, C.[Chun],
Analysis of the electrical impedance tomography algorithm based on
finite element method and Tikhonov regularization,
ICWAPR14(36-42)
IEEE DOI
1402
Electrodes
BibRef
Maimaitijiang, Y.,
Roula, M.A.,
Sobeihi, K.,
Watson, S.,
Williams, R.J.,
Griffiths, H.,
A parallel implementation of a Magnetic Induction Tomography: Image
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0911
BibRef
Isa, M.D.[Mohd Daud],
Rahmat, M.F.[Mohd Fua'ad],
Application of Digital Imaging Technique in Electrical Charge
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ICMV09(277-281).
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0912
BibRef
He, S.J.[Shi-Jun],
Wang, H.X.[Hua-Xiang],
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A 3D Image Reconstruction Algorithm of Electrical Capacitance
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CISP09(1-4).
IEEE DOI
0910
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Huang, J.J.[Ji-Jer],
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The effect of electrode-skin interface model in electrical impedance
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ICIP98(III: 837-840).
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9810
BibRef
Martin, T.,
Idier, J.,
A FEM-Based Nonlinear MAP Estimator in Electrical Impedance
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ICIP97(II: 684-687).
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BibRef
9700
Shallof, A.M.,
Barber, D.C.,
Imaging the complex conductivity in electrical impedance tomography,
ICIP96(III: 543-546).
IEEE DOI
9610
BibRef
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Tomographic Image Generation, Cone-Beam, Fan-Beam, Helical, Spiral Reconstruction .